Fixing algorithm of Kinect depth image based on non-local means

Non-Local Means Hole Repair Algorithm Based on Adaptive Block

“…Zhang et al [14] introduced the statistical nearest neighbors distance measurement (SNNDM) into the NLM filter (NLM-SNN) to measure the distance between two patches, thereby enhancing perceived image quality. Lin et al [15] introduced a fixing algorithm for the Kinect depth image based on nonlocal means (NLM), utilizing weights calculated on the corresponding grayscale image through a distance factor and a value-consistency factor to fill holes in the depth image. These methodologies collectively leverage diverse strategies in hole repair techniques to effectively address challenges in in-depth image repair.…”

“…Additionally, the method in [14] combines the statistical nearest neighbor distance measurement so that it is more complex. In contrast, the approach proposed by [15], inspired by [14], integrates distance factors to enhance restoration efficiency for Kinect cameras but encounters challenges in intricate parameter adjustments. Drawing inspiration from [15], this paper exploits the structural similarity between grayscale and depth images.…”

“…In contrast, the approach proposed by [15], inspired by [14], integrates distance factors to enhance restoration efficiency for Kinect cameras but encounters challenges in intricate parameter adjustments. Drawing inspiration from [15], this paper exploits the structural similarity between grayscale and depth images. It utilizes joint grayscale images to estimate depth values within hole regions and applies the non-local means algorithm [14][15][16][17][18][19][20][21] for effective hole repair.…”

“…Drawing inspiration from [15], this paper exploits the structural similarity between grayscale and depth images. It utilizes joint grayscale images to estimate depth values within hole regions and applies the non-local means algorithm [14][15][16][17][18][19][20][21] for effective hole repair. Successful restoration is achieved through the application of structural blocks and NLM-based hole filling.…”

“…(2) By introducing intelligent block factors, we enable the automatic determination of optimal search and repair patch sizes for voids of different dimensions, resulting in a significant reduction of the intricate debugging efforts in engineering applications. The strategy of improving the performance of the algorithm by manipulating the pixel weights is discussed in [5,[14][15][16][17]. Nonetheless, it is worth noting that the acquisition of these weights relies predominantly on manual calibration, a process that demands a substantial investment of time.…”