Significant attention in computer vision has been focused on developing robust and efficient edge detection algorithms. Edge detection approaches, which span from traditional edge detection algorithms based on differential operations to cutting-edge edge detection algorithms based on neural networks, have significantly contributed to security, environmental sensing, and healthcare. Because image processing methods recover edge information, the availability of pre-complete photographs of the target object is necessary for conventional edge extraction. Consequently, the success of edge identification depends on the quality of the input image. However, standard optical imaging technologies have difficulty obtaining clear images of the target in complicated settings, such as those with objects buried behind fog, murky water, and biological tissues, especially in scenes with high light pollution. Edge detection quality in the final image may suffer as a result.
Edge-sensitive single-pixel imaging is introduced in the study published in Intelligent Computing. The novel method is particularly useful for detecting object edges properly despite noise when acquiring good images through standard optical methods is difficult due to variables such as severe light pollution.
The viability of SI-based edge detection algorithms has just recently been established. Without preliminary imaging or post-processing, a high-quality edge extraction method is provided using direct noise-resistant edge-sensitive single-pixel imaging (ESI). ESI illuminates an object with carefully crafted modulation patterns to extract its edges. Hadamard single-pixel imaging (HSI) involves projecting a corresponding set of Hadamard basis patterns onto a single pixel to generate a full image of an object. ESI obtains modulation patterns at the margins of the Hadamard basis patterns by convolving them with second-order differential operators. This approach directly acquires edge-sensitive Hadamard spectra of the object’s edges to detect edges, bypassing the need for any preexisting imaging. ESI uses binary modulation patterns to speed up edge detection and boost the signal-to-noise ratio (SNR).
An SESI edge detection technique was developed, using half as many modulation patterns as ESI but still quickly detecting edges. As a result, SESI can see edges in half the time, making SI-based edge detection more practical. The Laplacian and the Laplacian of Gaussian (LoG) are two examples of common second-order differential operators, and they take up most of the discussion here. Both theoretical and practical evaluations confirm their impact on the outcomes of edge detection simulations and experiments. Despite substantial background noise, these tests demonstrate that ESI and SESI can directly extract sharp edges from images.
Computational imaging in the form of SI is used to tailor the scene’s lighting to a given objective. In this research, the illumination patterns were created with the particular edge detection objective in mind. End-to-end optimized computational imaging, which also creates lighting patterns for a specific job (such as edge detection), is analogous to this work. Edge detection illumination patterns are built using a mathematical model that is both deterministic and interpretable. In contrast, end-to-end optimization illumination patterns are imagined using data-driven artificial intelligence, which typically involves optimization. End-to-end optimization has a high bar for achieving global optimality. This study focuses only on the lighting patterns generated by a pair of representative second-order differential operators.
Traditional SI acquires an object’s picture by projecting its matching modulation basis patterns and then uses an inverse transform or compressive sensing image recovery technique to rebuild the target image.
Typical Hadamard single-pixel imaging patterns were convolved using second-order differential operators to create the modulation patterns developed by the researchers. The noise immunity of this differential edge detection method is much improved, allowing for the clear and accurate detection of edges. Particularly impressive is the method’s ability to detect edges in real-time, even on moving objects, indicating its promise for use in covert security checks in invisible spectrums. The research also presents a one-round variant of the new method, which cuts the detection time in half by using fewer modulation patterns for edge detection. Despite this simplification, the system still uses fewer modulation patterns and has a higher signal-to-noise ratio than previously published edge detection schemes.
By pre-coding modulation patterns, the novel technology can produce immediate results in an “image-free” fashion, allowing for a large range of applications in image processing. As a result, homomorphic filtering and other image-processing techniques can be incorporated with less interference from background noise. Future improvements are expected to include the researchers’ exploration of end-to-end optimization and the optimization of the illumination patterns used in this work.
It was explained how the Laplacian and LoG operators affect the resilience of the ESI schemes. Simulation studies showed that the Laplacian ESI and the LoG ESI have similar noise robustness regarding SNRs, while the Laplacian ESI has sharper edges. The experimental findings agreed with the simulated results. The LoG ESI created rougher edges. The proposed ESI methodology provides an alternate method for retrieving the object’s edge image, and the idea that conventional image processing techniques may be pre-coded into modulation patterns and then used to provide direct results in an “image-free” fashion is provided as creative fodder. This additional dimension is significant as pre-coded modulation patterns are immune to disturbances and noise in the surrounding environment. Pre-coding is just one of many image-processing techniques that could be used for enhanced outcomes, including homomorphic filtering. The light patterns developed in this work can be fine-tuned and used as a jumping-off point for full system optimization.
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Dhanshree Shenwai is a Computer Science Engineer and has a good experience in FinTech companies covering Financial, Cards & Payments and Banking domain with keen interest in applications of AI. She is enthusiastic about exploring new technologies and advancements in today’s evolving world making everyone's life easy.
