Precise depth map upsampling and enhancement based on edge‐preserving fusion filters

Abstract

A texture image plus its associated depth map is the simplest representation of a three-dimensional image and video signals and can be further encoded for effective transmission. Since it contains fewer variations, a depth map can be coded with much lower resolution than a texture image. Furthermore, the resolution of depth capture devices is usually also lower. Thus, a low-resolution depth map with possible noise requires appropriate interpolation to restore it to full resolution and remove noise. In this study, the authors propose potency guided upsampling and adaptive gradient fusion filters to enhance the erroneous depth maps. The proposed depth map enhancement system can successfully suppress noise, fill missing values, sharpen foreground objects, and smooth background regions simultaneously. Their experimental results show that the proposed methods perform better in terms of both visual and subjective metrics than the classic methods and achieve results that are visually comparable with those of some time-consuming methods.

New Progress in Intelligent Picking: Online Detection of Apple Maturity and Fruit Diameter Based on Machine Vision

“…To ensure accurate and swift measurement of apple diameter, primary attention was given to correcting distortions in the color images and repairing depth data. Current conventional depth filtering methods, such as Gaussian filtering, median filtering, temporal filtering, spatial filtering, and edge-preserving filtering, often necessitate whole-image processing, resulting in slower processing speeds [25]. Considering our exclusive focus on the depth data of the detected apple target area, we adopted a localized approach that emphasized the removal and completion of depth data in the identified fruit target region.…”

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“…Filtering based methods employ the color information of color photo or texture information of texture image with various edge-preserving filters. Chang et al [2] use potency guided upsampling and adaptive gradient fusion filters to enhance the erroneous depth images to refine the upsampled depth results. Qiao et al [3] construct a feature-based bilateral filter (FBF) for the interpolation, by using the extracted RGB shallow and multi-layer features to improve the upsampled depth image quality.…”