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In today’s digital age, the tourism and homestay industry is facing unprecedented opportunities and challenges. With the continuous expansion of the tourism market, tourists’ demands for accommodation experience are becoming increasingly diverse and personalized. Traditional design methods often struggle to quickly respond to these changes, fully showcase regional characteristics, and achieve efficient personalized customization. At the same time, how to integrate modern technology while retaining traditional charm to enhance the attractiveness and comfort of homestays has become an urgent problem to be solved. This paper aims to explore in depth how to use this technological framework to optimize the decoration design of tourist homestays, in order to create living spaces that retain traditional charm and are rich in modern technology. By utilizing digital image network computing technology, it is possible to conduct 3D modeling and virtual simulation of the indoor space of homestays. By simulating different furniture placement and spatial separation schemes, combined with tourist behavior analysis data, the optimal spatial layout scheme is automatically recommended to achieve maximum space utilization and a comfortable living experience. This technology can analyze a large number of color matching cases, combined with the cultural characteristics of the area where the homestay is located, and apply the principles of color psychology to provide personalized color schemes for homestay design. Meanwhile, through style recognition and recommendation algorithms, designers can quickly locate and integrate traditional and modern elements to create unique decorative styles.

Single Image Super-Resolution (SISR) has seen significant advancements with the advent of deep learning techniques. However, many existing approaches face challenges such as high computational costs, poor generalization to unseen data and dependence on large paired datasets. This paper proposes a novel, lightweight Parallel Super-Resolution Convolutional Neural Network (PSRCNN) designed to address these limitations. PSRCNN leverages parallel feature extraction, a transposed convolutional upsampling layer and an efficient feature fusion strategy to balance performance and efficiency. Rigorous evaluations on established benchmark datasets demonstrate that PSRCNN achieves competitive performance, particularly in terms of the Structural Similarity Index (SSIM), a metric closely aligned with human visual perception. Moreover, the model showcases a significant advantage in computational efficiency, requiring fewer parameters than many recent Super-Resolution (SR) methods. PSRCNN presents a promising approach to SISR, demonstrating the potential of parallel CNN architectures for image SR tasks, as validated by ablation studies confirming the effectiveness of this design in enhancing image reconstruction quality. This approach is open to further enhancement.

This paper presents an efficient general purpose search algorithm for alignment and an applied procedure for IC print mark quality inspection. The search algorithm is based on normalized cross-correlation and enhances it with a hierarchical resolution pyramid, dynamic programming, and pixel over-sampling to achieve subpixel accuracy on one or more targets. The general purpose search procedure is robust with respect to linear change of image intensity and thus can be applied to general industrial visual inspection. Accuracy, speed, reliability, and repeatability are all critical for the industrial use. After proper optimization, the proposed procedure was tested on the IC inspection platforms in the Mechanical Industry Research Laboratories (MIRL), Industrial Technology Research Institute (ITRI), Taiwan. The proposed method meets all these criteria and has worked well in field tests on various IC products.

This paper documents the development and first offering of an undergraduate course in Digital Image Processing at the Rey Juan Carlos University, Madrid (Spain). The paper describes how the appropriate introduction of main Algorithm Design Techniques can successfully assist the students to achieve a comprehensive understanding of image operations and related algorithms. Image processing problems offer a natural way to present real world problems where the students can use their algorithmic knowledge. Furthermore, image processing solutions are needed from a methodological development and require efficient well-designed algorithms. This paper presents an effort in the integration of Algorithm Design Techniques in a Digital Image Processing course with a very practical scope.

Breast cancer (BC) is one of the leading causes of death in adult women worldwide and the best way to reduce mortality and improve prognosis is through early diagnosis. Thus, it is necessary to optimize diagnostic methods; one option could be the automatic detection of patterns in 1D-II. In that respect, through recent analysis of unidimensional Immunoblot Images (1D-II), it was possible to distinguish between women with and without breast disease using as a discrimination criterion the presence of autoantibodies (bands) in their blood. However, the analysis of 1D-II is a difficult task even for an expert, generating great subjectivity and complexity in the process of interpretation.

In the present study, a semi-automatic methodology for the bands’ analysis contained in the 1D-II’s was implemented and evaluated, the bands were extracted using digital image processing techniques. This was possible through the recognition of banding patterns represented as time series to distinguish between three classes: women with breast cancer (BC), women with benign breast pathology (BBP) and women without breast pathology (H). The classification was performed using the machine learning algorithm k-nearest neighbors (KNN) with different parameters over the time series representation.

The semi-automatic method here presented was able to reduce the time, complexity and subjectivity of the image analysis with the performance metrics compared, obtaining similar percentages for both representations. With the traditional analysis, binary representation [Accuracy 72.8%, Precision 73.42% for three classes (BC, BBP and H) and Accuracy 90.91% Accuracy 92.55% Sensitivity 93.57% and Specificity 92.99% for two classes (BC and H)], versus Time series representation [Accuracy 66.4%, Precision 67.07% for three classes (BC, BBP and H) and Accuracy 86.36% Accuracy 87.31% Sensitivity 95.86% and Specificity 85.56% for two classes (BC and H)].

An exact analytical approach to n-dimensional discrete images, objects, and surfaces is given. Objects are defined as finite grid point sets of Zⁿ. Using the 2n-neighborhood of Zⁿ, components are maximal connected sets of object points. Formulas are given for count measures of object (points, edges, faces, cells, marginal elements, etc.), and the Euler number ψ⁽ⁿ⁾. Such measures are defined in Z³ with respect to volume, surface content, and the mean curvature integral in E³. The generalization for Zⁿ allows the definition of similarities between n-dimensional objects. The Euler number and other object characteristics can be expressed only by numbers of marginal elements of single object surfaces. Finally, a general formula for ψ⁽³⁾ is given.

A low complexity parallel filter architecture for image processing applications is presented. The proposed approach makes use of the transposition and custom poly-phase sub-filters design in ‘Fast FIR’ filter implementation technique to exploit the correlation among input samples by processing the intermediate signals depicting low entropy through low complexity multipliers. Despite lower complexity, the proposed approximate design performs very close to the corresponding full precision implementation as evident from the provided experimental results. The experimental evidence shows that other related efforts reported in the literature are extremely sensitive to the approximation and yields unusable results due to the propagation of error without bounds. The proposed approximate design, on the other hand, limits the inclusion of error to as low as 4% of the data samples with high Signal-to-Noise Ratio (SNR) values on average. This has been achieved through design of custom polyphase sub-filters without recursive stage and proved to be feasible irrespective of the number of kernel taps. The design efficiently maps on a Xilinx Field Programmable Gate Arrays (FPGA) and conserves chip resources by at least 6% for kernels with asymmetric fixed coefficients and 29% for symmetric ones.

Digital image processing (DIP) becomes a common tool for analyzing engineering problems by fast, frequent and noncontact method of identification and measurement. An attempt has been made in the present investigation to use this method for automatically detecting the worn regions on the material surface and also its measurement. Brass material has been used for experimentation as it is used generally as a bearing material. A pin on disc dry sliding wear testing machine has been used for conducting the experiments by applying loads from 10 N to 50 N and by keeping sliding distance and sliding speed constant. After testing, images are acquired by using 1/2 inch interline transfer CCD image sensor with 795(H)$\ast$896(V) spatial resolution of 8.6$\mu$m (H)$\ast$8.3$\mu$m (V) unit cell. Denoising has been done to remove any possible noise followed by contrast stretching to enhance image for wear region extraction. Segmentation tool was used to divide the worn and unworn regions by identifying white regions greater than a threshold value with an objective of quantifying the worn surface for tested specimen. Canny edge detection and granulometry techniques have been used to quantify the wear region. The results revel that the specific wear rate increases with increase in applied load, at constant sliding speed and sliding distance. Similarly, the area of worn region as identified by DIP also increased from 42.7% to 69.97%. This is because of formation of deeper groves in the worn material.

Researchers are always trying to explore, use resources so that the machining performances become more efficient, economically viable, and environment-friendly which are in general, conflicting in nature. In this investigation, surface finish, material removal rate, and tool wear are considered as responses, and cutting speed, feed, and depth of cut are considered as input factors for optimization of AISI 4140 alloy steel during CNC dry turning. In most of the previous researches, weights of the responses are considered arbitrarily. But, in this investigation, weighted ratio analysis is considered as an optimizing tool whose weights are determined by the Eigenvectors obtained from the principal component analysis of the experimental data. This weighted ratio analysis technique requires very few computations compared to others, and even it can be performed without a computer. Surface texture of the tool wear is measured using image processing of the wear surface. First, a metallurgical microscope captures an image of the sharp tool (unworn), and RGB values of the gray scale image are noted, which acts as a reference. After machining, RGB values of the wear surface from the gray scale image are noted. Comparing these two RGB values, the amount of flank wear of the tool is determined. It is observed that the error is within 10% and therefore it is accepted.

Deep submicron conventional complementary metal oxide semiconductor (CMOS) technology is facing various issues such as high static power consumption due to the increasing leakage currents. In recent years, spin-based technologies like magnetic tunnel junctions (MTJ) have emerged and shown some fascinating features to overcome the aforesaid issues of CMOS technology. The hybrid MTJ/CMOS circuits offer low power consumption, nonvolatility, and high performance. This paper proposes two novel hybrid MTJ/CMOS approximate full-adder circuits (AXMA) for low power approximate computing-in-memory architectures. The proposed AXMAs offer low area, high sensing speed, considerable lower energy consumption, and the lowest power delay product (PDP) than the considered antecedent counterparts. The proposed AXMAs also introduce the advantage of full nonvolatility to the systems. This feature allows the system to be powered off during the idle modes in order to reduce the static power without the need for any retention parts or loss of data. Applications of the proposed AXMAs in digital image processing and their effect on the quality of images considering some relevant metrics like peak signal-to-noise ratio (PSNR) and mean structural similarity (MSSIM) are also investigated using the MATLAB software.

EDMA (Enhanced Direct memory access) is an important method to transmit data in Digital Signal Processer (DSP) for high speed and real-time processing of digital signal, such as digital image processing, radar signal recognition. However existing DMAs do not use data pre-computation and they are incapable of satisfying the increasing demands of signal processing, target recognition or scientific computing applications. In this paper, we propose a new enhanced DMA design with data pre-computing for digital image processing, which provides more efficient data transfers in modern DSP by integrating several useful arithmetic units for data preprocessing. In comparison to traditional EDMA, the proposed design reduces the DSP’s burden of simple calculations and increases the efficiency of data transmission for improved performance..

The rhythm of a scanned ECG paper can be interpreted by extracting the first derivative of the signal. This paper discusses a first derivative method to classify five different rhythms, namely: Normal Sinus Rhythm, Sinus Bradycardia, 1st Degree AV Block, Atrial Flutter and the Asystole. The PQRST Wave complex is mapped and the different intervals are identified. Unlike other methods where learning is needed and quite a number of data are required, the method described here used rule-based method. It is capable of interpreting data with minimal samples. Based on the studied data, the method was able to produce a 95% accuracy for samples with grids that do not perfectly lie in time axis.

Every day massive quantities of two-dimensional documents are produced, stored and transmitted. Digital color images are the most prominent type of data in this category. To process these objects a variety of powerful and sophisticated wavelet-based schemes have been developed and implemented. Considering that digital color images are vector objects with three components, they can be suitably processed in the context of a multichannel Multi Resolution Analysis.

In this work we focus on a new vector approach to multichannel wavelet analysis of color images based on several classes of full rank filters, showing the results of a wide experimentation.

With the development of comprehensive mechanized coal mining technology, emerging technology has been applied to more and more coal mining automation. So does the digital image processing technology. The rapid development of image processing technology has been widely used in various fields. The particle aims at the actual situation of coal mine working face, combines with digital image processing technology, and discusses the coal mine working face of the digital image processing technology. It brings the image threshold segmentation, histogram equalization technology into the coal mining face automation system.