Abstract: A method performed by an electronic device is described. The method includes obtaining an input image. The input image includes image noise. The method also includes removing the image noise from the input image to produce a noise-removed image. The method further includes avoiding enhancing the image noise by performing edge detection on the noise-removed image to produce edge information. The method additionally includes producing a processed image based on the input image and the edge information.

Abstract: A method performed by an electronic device is described. The method includes obtaining an image. The method also includes normalizing a set of frequency band amplitudes of the image based on a plurality of normalization gains to produce a normalized set of frequency band amplitudes. The method further includes producing a processed image based on the normalized set of frequency band amplitudes.

Abstract: Certain embodiments relate to systems and methods for dynamic color shading correction, which can estimate the color shading in a captured image on the fly. The color shading may be estimated from the scene statistics of the captured image, and the estimated shading may be used for color shading correction. The color shading estimation method may separate out the color shading component from the actual image content by its unique characteristic in the gradient domain.

Abstract: Certain aspects relate to systems and techniques for correction of color artifacts including both color aberration and color spot artifacts using a unified framework. For example, both color aberration and color spot artifacts can be corrected using a post-processing method implementing directional median filtering on chroma channels. A pixel-by-pixel correction ratio map can be built by analyzing the luma and chroma components of the image to indicate a type of color artifact associated with each pixel in the image, and a directional median filter can be selected for each pixel based on the corresponding correction ratio map value.

Abstract: Certain embodiments relate to systems and methods for dynamic color shading correction, which can estimate the color shading in a captured image on the fly. The color shading may be estimated from the scene statistics of the captured image, and the estimated shading may be used for color shading correction. The color shading estimation method may separate out the color shading component from the actual image content by its unique characteristic in the gradient domain.

Abstract: Certain aspects relate to systems and techniques for correction of color artifacts including both color aberration and color spot artifacts using a unified framework. For example, both color aberration and color spot artifacts can be corrected using a post-processing method implementing directional median filtering on chroma channels. A pixel-by-pixel correction ratio map can be built by analyzing the luma and chroma components of the image to indicate a type of color artifact associated with each pixel in the image, and a directional median filter can be selected for each pixel based on the corresponding correction ratio map value.

Abstract: A method recovers an uncorrupted low-rank matrix, noise in corrupted data and a subspace from the data in a form of a high-dimensional matrix. An objective function minimizes the noise to solve for the low-rank matrix and the subspace without estimating the rank of the low-rank matrix. The method uses group sparsity and the subspace is orthogonal. Random subsampling of the data can recover subspace bases and their coefficients from a much smaller matrix to improve performance. Convergence efficiency can also be improved by applying an augmented Lagrange multiplier, and an alternating stepwise coordinate descent. The Lagrange function is solved by an alternating direction method.

Abstract: A method recovers an uncorrupted low-rank matrix, noise in corrupted data and a subspace from the data in a form of a high-dimensional matrix. An objective function minimizes the noise to solve for the low-rank matrix and the subspace without estimating the rank of the low-rank matrix. The method uses group sparsity and the subspace is orthogonal. Random subsampling of the data can recover subspace bases and their coefficients from a much smaller matrix to improve performance. Convergence efficiency can also be improved by applying an augmented Lagrange multiplier, and an alternating stepwise coordinate descent. The Lagrange function is solved by an alternating direction method.