A new conjugate gradient method based on a modified secant condition with its applications in image processing

We propose an effective conjugate gradient method belonging to the class of Dai–Liao methods for solving unconstrained optimization problems. employ a variant modified secant condition and introduce new parameter by problem. The problem combines well-known features linear using some penalty functions. This takes advantage function information as well provide iterations. Our proposed is globally convergent under mild assumptions. examine ability real-world problems from image processing field. Numerical results show that efficient in sense PSNR test. also compare our with existing algorithms collection CUTEr its efficiency.