Reflection Removal Using Multiple Polarized Images with Different Exposure Times

The Design of a Video Reflection Removal Method Based on Illumination Compensation and Image Completion Fusion

“…Some of them also combine image information and multiscale gradient information with human perception-inspired loss functions [21] to accomplish reflection removal from images with machine learning. There are also some studies that use multiple polarized images taken at different exposure times in order to achieve high accuracy reflection removal in high dynamic range scenes [16] and minimize the under/over exposure pixel problem of the captured images by introducing a weighted non-negative matrix decomposition (WNMF) with full variation regularization. These multi-image methods lead to better reflection removal results due to the availability of image information from different viewpoints.…”

“…Video reflection removal. Motion cues are also often used to handle video reflection removal [15][16][17][18][19][20][21][22]. Information about the relative displacement of the environment (i.e., reflection dynamics) captured in the image sequence and separated from the video frames is used to help image sequences separate reflection layers and reduce reflection artifacts using temporal coherence constraints.…”

“…Reflection removal involves separating the layers of an image into the background and reflection layers. There are various methods for removing reflections, including single image reflection removal (SIRR) [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15], multi-image reflection removal [16][17][18][19][20][21], video reflection removal [22][23][24][25], light field camera reflection removal [26][27][28][29], and special function camera (sensor) reflection removal [30,31]. The image removal model is shown in Figure 1.…”

“…In another study, a solution was proposed to minimize the issue of under/over exposed pixels in captured images. This was achieved by using multiple polarized images taken at different exposure times [16] and introducing the weighted nonnegative matrix decomposition of full change regularization. It should be noted, however, that these methods often require a longer sample training time.…”

“…The target area recognition step of this method's detection speed and reflection removal integrity may be guaranteed by coupling these two activities. In contrast to [4,16,26,28], our technique does not need the building of a complicated machine learning network or a substantial amount of sample training, and the process for acquiring pictures is also rather straightforward. Our contributions are summarized as follows:…”