전병우 교수

This paper proposes two decision fusion-based multitemporal classifiers, namely, the jointly likelihood and the weighted majority fusion classifiers, that are derived using two different definitions of the minimum expected cost. Without any overhead incurred by multitemporal processing, a user-selected conventional pixelwise classifier makes local class separately using each temporal data set, and the multitemporal classifiers make the global class decisions by optimally summarizing those local

A contextual classifier which can utilize both spatial and temporal interpixel dependency contexts is investigated. After spatial and temporal neighbors are defined, a general form of maximum a posterior spatiotemporal contextual classifier is derived. This contextual classifier is simplified under several assumptions. Joint prior probabilities of the classes of each pixel and its spatial neighbors are modeled by the Gibbs random field. The classification is performed in a recursive manner to al

This paper addresses a classification problem in which class definition through training samples or otherwise is provided a priori only for a particular class of interest. Considerable time and effort may be required to label samples necessary for defining all the classes existent in a given data set by collecting ground truth or by other means. Thus, this problem is very important in practice, because one is often interested in identifying samples belonging to only one or a small number of clas

This paper proposes a novel blocking artifacts reduction method based on the notion that the blocking artifacts are caused by heavy accuracy loss of transform coefficients in the quantization process. We define the block boundary discontinuity measure as the sum of the squared differences of pixel values along the block boundary. The proposed method compensates for selected transform coefficients so that the resultant image has a minimum block boundary discontinuity. The proposed method does not

Known to be structured in several patterns at the same time, the prior image of interest is always modeled with the idea of enforcing multiple constraints on unknown signals. For instance, when dealing with a hyperspectral restoration problem, the combination of constraints with piece-wise smoothness and low rank has yielded promising reconstruction results. In this paper, we propose a novel mixed-noise removal method by employing 3D anisotropic total variation and low rank constraints simultane

This paper proposes a novel blocking artifacts reduction method which is based on the notion that the blocking artifacts are present in images due to heavy accuracy loss of transform coefficients in the quantization process. We define the block boundary discontinuity measure as the sum of the squared differences of pixel values along the block boundary. The proposed method makes correction of the selected transform coefficients so that the resultant image has minimum block boundary discontinuity

In compressive sensing (CS) of images, a block‐based framework is preferred to avoid the huge memory and computation required for a frame‐based approach. However, the recovered image suffers from blocking artefacts due to independent block processing, especially at a low subrate. As a result of this reported work the artifacts are reduced by weighted averaging adopting two techniques: overlapped CS recovery and adaptive weighting. Simulation results show its improvement in both subjective and ob

A spatio-temporal contextual classifier that can utilize both spatial and temporal information is investigated. Experiments carried out with Landsat TM data are reported. They show that spatial correlation contexts are more useful than the other contexts. The use of the homogeneity test followed by a selective application of the contextual rule is more effective than the totally recursive case in the sense of both classification accuracy and computation. Classification performance is compared wi